We consider the challenging task of training models for image-to-video deblurring, which aims to recover a sequence of sharp images corresponding to a given blurry image input. A critical issue disturbing the training of an image-to-video model is the ambiguity of the frame ordering since both the forward and backward sequences are plausible solutions. This paper proposes an effective self-supervised ordering scheme that allows training high-quality image-to-video deblurring models. Unlike previous methods that rely on order-invariant losses, we assign an explicit order for each video sequence, thus avoiding the order-ambiguity issue. Specifically, we map each video sequence to a vector in a latent high-dimensional space so that there exists a hyperplane such that for every video sequence, the vectors extracted from it and its reversed sequence are on different sides of the hyperplane. The side of the vectors will be used to define the order of the corresponding sequence. Last but not least, we propose a real-image dataset for the image-to-video deblurring problem that covers a variety of popular domains, including face, hand, and street. Extensive experimental results confirm the effectiveness of our method. Code and data are available at https://github.com/VinAIResearch/HyperCUT.git

翻译：我们研究了图像到视频去模糊这一具有挑战性的模型训练任务，旨在从给定的单张模糊图像输入中恢复出对应的清晰图像序列。由于前向序列和反向序列均为合理解，帧序歧义成为干扰图像到视频模型训练的关键问题。本文提出了一种有效的自监督排序方案，能够训练高质量的图像到视频去模糊模型。与以往依赖顺序不变损失的方法不同，我们为每个视频序列显式分配顺序，从而避免顺序歧义问题。具体而言，我们将每个视频序列映射至潜在高维空间中的向量，使得存在一个超平面，对每个视频序列而言，从其原始序列和逆序序列中提取的向量分别位于该超平面两侧。这些向量所在侧将用于定义对应序列的顺序。此外，针对图像到视频去模糊问题，我们构建了一个涵盖人脸、手部、街道等多种常见场景的真实图像数据集。大量实验结果验证了本方法的有效性。代码与数据集已开源至 https://github.com/VinAIResearch/HyperCUT.git